论文标题
生成知识图构造：评论
Generative Knowledge Graph Construction: A Review
论文作者
论文摘要
生成知识图构造（KGC）是指利用构建知识图的顺序到序列框架的方法，该框架是灵活的，可以适应广泛的任务。在这项研究中，我们总结了生成知识图构造的最新进展。我们在不同的产生目标方面介绍了每个范式的优势和弱点，并提供了理论的洞察力和经验分析。根据审查，我们建议对未来的有希望的研究指示。我们的贡献是三重的：（1）我们为生成性kgc方法提供了详细的，完整的分类法； （2）我们对生成性kgc方法提供了理论和经验分析； （3）我们建议将来可以开发几个研究方向。
Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
